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Workflow mining: discovering process models from event logs
Abstract
Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.
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Cited by
Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs
Nandi, A., Mandal, A., Atreja, S., Dasgupta, G. B., & Bhattacharya, S. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939712 · Cited by 154
Survey on Models and Techniques for Root-Cause Analysis
Solé, M., Muntés-Mulero, V., Rana, A. I., & Estrada, G. (2017) · arXiv · doi:10.48550/arxiv.1701.08546 · Cited by 74
Jia, T., Wu, Y., Hou, C., & Li, Y. (2021) · 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre52982.2021.00021 · Cited by 55
MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs
Wang, X., Yang, L., Li, D., Ma, L., He, Y., Xiao, J., Liu, J., & Yang, Y. (2022) · Proceedings of the 38th Annual Computer Security Applications Conference · doi:10.1145/3564625.3567972 · Cited by 16
Metadata
- Authors
- van der Aalst, W., Weijters, T., & Maruster, L.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2004-09
- Volume
- 16
- Issue
- 9
- Pages
- 1128-1142
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2098250644
- Cited by
- 2,203
- References
- 0
- FWCI
- 96.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2004.47
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